Preview

Zhurnal Prikladnoii Spektroskopii

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Flight Parameter Setting of Unmanned Aerial Vehicle Hyperspectral Load

Abstract

Correct flight parameters are critical for obtaining high-quality unmanned aerial vehicle (UAV) remote sensing images. For the UAV, the Rikola hyperspectral load needs to set the instrument's exposure time, UAV flight mode, flight altitude, and other issues when acquiring data. Using the control variable method, UAV Rikola hyperspectral images were collected under different parameters, and the gray-scale target and image's quantitative evaluation index was used to obtain the spectral curves of gray-scale targets, ground features, signal-to-noise ratio (SNR), information entropy, and sharpness of imagery. The results of the comparative analysis show: the vegetation hyperspectral data quality was better when determining the Rikola hyperspectral exposure time using the 64% diffuse plate; UAV hover mode and cruise mode had little impact on data quality; when the flight altitude was within 100 m above ground level, the higher the flying height, the better the data quality. This study therefore provides evidence for obtaining high-quality data using UAV hyperspectral load.

About the Authors

W. Tian
College of Mechanical and Electrical Engineering, Shi Hezi University; Research Center for Space Information Engineering Technology
China

Xinjiang



Q. Zhao
College of Information Science and Technology, Shi Hezi University; Research Center for Space Information Engineering Technology
China

Xinjiang



Y. Ma
College of Information Science and Technology, Shi Hezi University; Research Center for Space Information Engineering Technology
China

Xinjiang



X. Long
College of Mechanical and Electrical Engineering, Shi Hezi University; Research Center for Space Information Engineering Technology
China

Xinjiang



X. Wang
College of Information Science and Technology, Shi Hezi University; Research Center for Space Information Engineering Technology
China

Xinjiang



References

1. H. Y. Cen, L. Wan, J. P. Zhu, J. Plant Methods, 15, 1 (2019).

2. M. Li, Y. Q. Huang, X. M. Li, D. X. Peng, J. X. Xie, J. Trans. Chin. Soc. Agric. Eng., 34, No. 4, 108–114 (2018).

3. K. Uto, H. Seki, G. Saito, et al. Workshop on Hyperspectral Image & Signal Processing: Evolution in Remote Sensing (2017).

4. P. Mark, B. Dmitry, G. Kevin, K. J. Gaston, F. Gonzalez, J. Sensors, 18, No. 7, 20–26 (2018).

5. X. L. Hou, H. B. Luo, P. P. Zhou, J. Infrared Laser Eng., 46, No. 7, 263–269 (2017).

6. J. Y. Ning, The Research on Realization of the Auto-exposure Algorithm Based on Entropy, D. First Research Institute of China Aerospace Science and Technology Corporation, 75–87 (2016).

7. P. Walczykowski, K. Siok, A. Jenerowicz, J. Int. Arch. Photogrammetry, Remote Sensing Spatial Inform. Sci., 41, 1065–1069 (2016).

8. Y. Huang, X. H. Chen, Y. L. Liu, Z. H. Huang, M. Sun, Y. C. Su, J. Anhui Agric. Sci., 46, No. 11, 170–173 (2018).

9. B. Liu, Classification of Crops Based on UAV Remote Sensing Images, D. University of Chinese Academy of Sciences, 29–44 (2019).

10. J. Lee, S. Sung, J. Spatial Inform. Res., 24, No. 2, 141–154 (2016).

11. K. He, Research on Key Technologies of Aerial Remote Sensing System Based Small UAV, D. Chongqing University, 15–32 (2017).

12. J. J. Yang, Y. Q. Zhao, C. Yi, J. C. W. Chan, J. Remote Sens., 9, No. 4, 305 (2017).

13. X. H. Cao, X. H. Li, Z. H. Li, L. C. Jiao, Int. J. Remote Sens., 38, No. 12, 3656–3668 (2017).

14. X. Y. Wang, J. Q. Li, J. Li, J. IOP Conf. Ser. Mater. Sci. Eng., 466, 12–53 (2018).

15. H. Saari, I. Pölönen, H. Salo, et al. J. Proc. SPIE – The International Society for Optical Engineering, 8889, 6 (2013).

16. A. M. Poncet, K. Thorsten, B Christian, et al. J. Remote Sens., 16, 11 (2019).

17. B. Zhu, X. H. Wang, L. L. Tang, C. R. Li, J. Remote Sens. Technol. Appl., 25, No. 2, 303–309 (2010).

18. Q. Chen, Y. Q. Xue, J. Remote Sens., 4, 284–289 (2000).

19. B. R. Corner, Int. J. Remote Sens., 24, No. 4, 689–702 (2003).

20. D. Y. Tsai, Y. Lee, E Matsuyama, J. Digital Imaging, 21, No. 3, 338–347 (2008).

21. H. Gao, Q. G. Miao, J. C. Yang, Z. X. Ma, J. IEEE Access, 99, 1–5 (2018).

22. B. Y. Qin, R. Shang, S. Y. Li, B. Q. Hei, Z. W. Liu, Reliable Sharpness Automatic-Evaluation Method for Optical Remote Sensing Images, C. Image Processing & Analysis (2015).


Review

For citations:


Tian W., Zhao Q., Ma Y., Long X., Wang X. Flight Parameter Setting of Unmanned Aerial Vehicle Hyperspectral Load. Zhurnal Prikladnoii Spektroskopii. 2022;89(1):135-144.

Views: 197


ISSN 0514-7506 (Print)